Ismail Ben Ayed

Algorithms to better interpret medical images

Each day, clinicians are inundated with billions of images obtained from X-rays, ultrasound scans, magnetic resonance imagining, and tomography. A professor at the École de technologie supérieure (ÉTS), Ismail Ben Ayed, believes this is an untapped medical potential.

And we can understand why: Analyzing and interpreting all this data is a painstaking task! Take for example: "To calculate the ejection fraction of the heart [editor's note: heart pumping efficiency] a radiologist must study approximately 400 images in two dimensions, and on each image they must design the contours of the left ventricle to record approximations of the differences in volume, and then imagine everything in three dimensions. The whole process takes time," laments the engineer. Quite a lot of time...

The solution? Tap into artificial intelligence. "Between 2012 and 2015, artificial vision has seen major breakthroughs," states Mr. Ben Ayed. "Just think about Facebook's facial recognition program. Today, we are trying to do the same thing in the medical field. However, it is a lot more complex to have a machine identify a tumour than it is to recognize a cat or a table."

Using advanced algorithms, Professor Ben Ayed is attempting to introduce a portion of medical knowledge in computer programs to facilitate the work of surgeons and radiologists. When he worked at GE Healthcare, a company that provides medical services and technologies, Professor Ben Ayed perfected a software program that is able to evaluate the ejection fraction of the heart. He also developed an algorithm that automatically annotates the bones of the spinal column. These two programs are already being used in hospitals.

"We have resolved problems that are rather basic in comparison with what is facing us in future," predicts the researcher, who was recently named to the ÉTS research chair on artificial intelligence and medical imagery.

Ismail Ben Ayed dreams of creating algorithms that can detect what is invisible to the naked eye, and thus predict diseases. He cites as an example people with back problems. "We estimate that approximately 5% of patients who have an MRI for this type of pain present a risk of an abdominal aortic aneurism," he states. "If this happens, it causes immediate death. Imagine a program that would automatically indicate an abnormal diameter of the aorta, a sign that there is an imminent risk of rupture. We could save lives. We could also correlate images drawn from thousands of surgeries, which would allow us to prevent post-surgery problems in patients."